What is continuous analytics with RisingWave?

Continuous analytics means computing metrics, aggregations, and insights incrementally as data arrives, rather than running periodic batch queries. RisingWave maintains always-fresh materialized views that update automatically, enabling live dashboards and real-time decision-making.

How does RisingWave differ from traditional BI tools?

Traditional BI tools query static data snapshots. RisingWave continuously processes streaming data and maintains pre-computed results in materialized views, delivering sub-second query latency on always-current data without scheduled refreshes.

Use Case

Deliver Data Into Iceberg Tables with Zero Operational Overhead

Continuously ingest, transform, and write streaming data into managed Apache Iceberg tables — no orchestrators, no compactors, no extra services to manage.

Live Lakehouse Pipeline

continuous

Iceberg Tables

127

● all healthy

Rows Written/sec

285K

● steady throughput

Compaction Queue

● auto-managed

Data Freshness

45s

end-to-end latency

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for Real-time Analytics

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The Problem

Are Your Iceberg Tables Hours Behind Because ETL Runs Overnight?

Most Iceberg architectures rely on batch Spark jobs to land data. You schedule them every few hours, manage compaction separately, and pray the orchestrator doesn't fail. Your lakehouse data is never truly fresh.

With RisingWave

Write to Iceberg Continuously — Compaction Included

RisingWave writes to Apache Iceberg tables continuously as data arrives. Automated compaction, schema evolution, and exactly-once delivery are built in. Your queries always see fresh data.

Continuous Ingestion

Stream data from Kafka, CDC, and APIs directly into Iceberg tables. No batch jobs, no scheduling.

Managed Compaction

Automatic small-file compaction and data optimization. No manual Spark jobs, no maintenance windows.

Query-Ready in Seconds

Data lands in Iceberg within seconds, not hours. Your Snowflake, Trino, and Spark queries always see fresh results.

See RisingWave in Action: Streaming Lakehouse

See how RisingWave processes real data in real time — not a recording, not a simulation.

A crypto trading desk monitors Solana DEX activity across Jupiter, Raydium, and Orca. They need to detect whale movements and arbitrage opportunities before the rest of the market reacts — every block (~400ms) matters.

⚠By the time an analyst pulls data from a block explorer, the arbitrage window has closed and the whale has already moved.

LIVEdex_swaps

signature	slot	program	wallet	token_in	token_out	amount_in	amount_out	ts
5vGn...kQ3m	250847291	JUP6LkbZbjS1jKKwapdHNy74zcZ3tLUZoi5QNyVTaV4	7xKXa9m2pVEb3R8gT4wNdJfLqYsPnpR4n	SOL	BONK	14.2	1142857	2024-03-15T14:32:01.412Z
3kRp...wN8j	250847293	675kPX9MHTjS2zt1qfr1NYHuzeLXfQM9H24wFSUt1Mp8	7xKXa9m2pVEb3R8gT4wNdJfLqYsPnpR4n	USDC	BONK	8500	680000	2024-03-15T14:32:02.204Z
9mTx...bL5e	250847295	whirLbMiicVdio4qvUfM5KAg6Ct8VwpYzGff3uctyCc	7xKXa9m2pVEb3R8gT4wNdJfLqYsPnpR4n	SOL	BONK	6.8	537142	2024-03-15T14:32:03.891Z
2hYd...pK7v	250847294	JUP6LkbZbjS1jKKwapdHNy74zcZ3tLUZoi5QNyVTaV4	Bq4rJ7pVEcSan3k9dTx5NLzRmA8HYfJp2	SOL	JUP	245.5	18412.5	2024-03-15T14:32:02.687Z
7fWm...cR2a	250847296	675kPX9MHTjS2zt1qfr1NYHuzeLXfQM9H24wFSUt1Mp8	DxP8rNfvYk4G2mBz5QLr9TJa3nYhR6wKm	USDC	WIF	42000	14000	2024-03-15T14:32:04.118Z
4nBx...hT9q	250847297	JUP6LkbZbjS1jKKwapdHNy74zcZ3tLUZoi5QNyVTaV4	DxP8rNfvYk4G2mBz5QLr9TJa3nYhR6wKm	WIF	SOL	14000	251.3	2024-03-15T14:32:04.802Z

Streaming SQLRunning

▶Track whale accumulation patterns✓

CREATE MATERIALIZED VIEW whale_accumulation AS
SELECT
  wallet,
  token_out AS token,
  SUM(amount_out) AS total_amount,
  COUNT(*) AS swap_count,
  COUNT(DISTINCT program) AS dex_count,
  window_start
FROM TUMBLE(dex_swaps, ts, INTERVAL '30 SECONDS')
GROUP BY wallet, token_out, window_start
HAVING
  SUM(amount_in) > 10000
  OR COUNT(*) > 3;

▶Detect cross-DEX arbitrage✓

whale_accumulationauto-updating

wallet	token	total_amount	swap_count	dex_count	window_start
7xKX...pR4n	BONK	2613967	4	3	2024-03-15T14:32:00.000Z
DxP8...6wKm	WIF	14000	1	1	2024-03-15T14:32:00.000Z
Bq4r...Jp2	JUP	18412.5	1	1	2024-03-15T14:32:00.000Z

✓RisingWave detects wallet 7xKX...pR4n accumulating 2.3M BONK across 3 DEXes in 12 seconds. The desk repositions before the price impact fully propagates.

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Why RisingWave

Build a Lakehouse That's Always Fresh

Replace batch ETL with continuous streaming into Apache Iceberg — and eliminate the infrastructure that comes with it.

Eliminate Batch ETL Complexity

Stop managing Airflow DAGs, Spark clusters, and cron schedules just to land data in your lakehouse. RisingWave replaces all of it with a single streaming SQL statement.

Minutes-Fresh Data (Not Hours)

Data arrives in your Iceberg tables within seconds of being produced. Analysts and ML models work with the freshest data possible, not yesterday's snapshot.

Zero Infrastructure to Manage

No separate compaction service, no orchestrator, no manual maintenance. RisingWave handles ingestion, transformation, compaction, and delivery as one managed system.

Deliver Data Into Iceberg Tables with Zero Operational Overhead

Are Your Iceberg Tables Hours Behind Because ETL Runs Overnight?

Write to Iceberg Continuously — Compaction Included

Build a Lakehouse That's Always Fresh

Ready to Build a Streaming Lakehouse?